Post-Acquisition Adaptive Reconstruction of MRI Data

ABSTRACT

An improved method for reconstruction of medical images includes the acquisition of k-space data through MRI imaging. Subsequently, subsets of the k-space data are transformed into intermediate images by performing an inverse Fourier transform on selected sets. These intermediate images are saved to a PACS or other memory storage, and can be recalled later to reconstruct an image. By selecting various intermediate images, a user can vary both the spatial and temporal resolution of the reconstructed image after acquisition, thereby providing adaptive reconstruction of images without the need to acquire new data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit U.S. nonprovisional application Ser.No. 12/056,908, filed Mar. 27, 2008, now pending, which claims thebenefit of U.S. Provisional patent application Ser. No. 60/920,355 filedon Mar. 27, 2007 and entitled Post-Acquisition Adaptive Reconstructionof MRI Data, which is hereby incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION

The field of the invention is magnetic resonance imaging (“MRI”) methodsand systems. More particularly, the invention relates to systems formanaging image data associated with MRI.

When a substance such as human tissue is subjected to a uniform magneticfield (polarizing field B.sub.0), the individual magnetic moments of theexcited nuclei in the tissue attempt to align with this polarizingfield, but precess about it in random order at their characteristicLarmor frequency. If the substance, or tissue, is subjected to amagnetic field (excitation field B.sub.1) which is in the x-y plane andwhich is near the Larmor frequency, the net aligned moment, M.sub.z.,may be rotated, or “tipped”, into the x-y plane to produce a nettransverse magnetic moment M.sub.t. A signal is emitted by the excitednuclei or “spins”, after the excitation signal B.sub.1 is terminated,and this signal may be received and processed to form an image.

When utilizing these “MR” signals to produce images, magnetic fieldgradients (G.sub.x, G.sub.y and G.sub.z) are employed. Typically, theregion to be imaged is scanned by a sequence of measurement cycles inwhich these gradients vary according to the particular localizationmethod being used. The resulting set of received MR signals aredigitized and processed to reconstruct the image using one of many wellknown reconstruction techniques.

The measurement cycle used to acquire each MR signal is performed underthe direction of a pulse sequence produced by a pulse sequencer.Clinically available MRI systems, for example, store a library of suchpulse sequences that can be prescribed to meet the needs of manydifferent clinical applications.

The MR signals acquired with an MRI system are signal samples of thesubject of the examination and are stored as “raw data”, which includesboth phase cycling and other data, as well as “k-space data”, whichrelates the signals to Fourier space, or what is often referred to inthe art as “k-space”. Each MR measurement cycle, or pulse sequence,typically samples portion of k-space along a sampling trajectorycharacteristic of that pulse sequence. Most pulse sequences samplek-space in a roster scan-like pattern sometimes referred to as a“spin-warp”, a “Fourier”, a “rectilinear” or a “Cartesian” seam Thespin-warp scan technique is discussed in an article entitled “Spin-WarpMR Imaging and Applications to Human Whole-Body Imaging” by W. A.Edelstein at al., Physics in Medicine and Biology, Vol. 25, pp. 751-756(1980). It employs a variable amplitude phase encoding magnetic fieldgradient pulse prior to the acquisition of MR spin-echo signals to phaseencode spatial information in the direction of this gradient. In atwo-dimensional implementation (2DFT), for example, spatial informationis encoded in one direction by applying a phase encoding gradient (Gy)along that direction, and then a spin-echo si to is acquired in thepresence of a readout magnetic field gradient (G_(x)) in a directionorthogonal to the phase encoding direction. The readout gradient presentduring the spin-echo acquisition encodes spatial information in theorthogonal direction. In a typical 2DFT pulse sequence, the magnitude ofthe phase encoding gradient pulse G_(y) is incremented (ΔG_(y)) in thesequence of measurement cycles, or “views” that are acquired during thescan to produce a set of k-space MR data from which an entire image canbe reconstructed.

There are many other k-space sampling patterns used by MRI systems.These include “radial”, or “projection reconstruction” scans in whichk-space is sampled as a set of radial sampling trajectories extendingfrom the center of k-space as described, for example, in U.S. Pat. No.6,954,067. The pulse sequences for a radial scan are characterized bythe lack of a phase encoding gradient and the presence of a readoutgradient which changes direction from one pulse sequence view to thenext. There are also many k-space sampling methods which are closelyrelated to the radial scan and which sample along a curved k-spacesampling trajectory rather than the straight line radial trajectory.Such pulse sequences are described, for example, in “Fast ThreeDimensional Sodium Mining”, MRM, 37:706-715, 1997 by F. E. Boada, et atand in “Rapid 3D PC-MRA Using Spiral Projection Imaging”, Proc. Soc.Magn. Resort. Med. 13 (2005) by K. V. Koladia et al and “SpiralProjection Imaging: a new fast 3D trajectory”, Proc. Intl. Soc. Mag.Reson. Med. 13 (2005) by J. G. Pipe and Koladia. In projectionreconstruction, the number of views needed to sample k-space determinesthe length of the scan and, if an insufficient number of views areacquired, streak artifacts are produced in the reconstructed image. Thetechnique disclosed in U.S. Pat. No. 6,487,435 induces such streaking byacquiring successive undersampled images with interleaved views andsharing peripheral k-space data between successive image frames. Thismethod of sharing acquired peripheral k-space data is known in the artby the acronym “TRICKS”.

There are also a number of variations of this straight line, radialsampling trajectory in which a curved path or a non-rectilinear patternis used to sample k-space. These include spiral projection imaging,SHELL imaging, and the periodically rotated overlapping parallel lineswith enhanced reconstruction (PROPELLER) fast spin-echo (FSE) techniquesdescribed by Pipe J G, Farthing V G, Forbes K P, “MultishotDiffusion-Weighted FSE Using PROPELLER MRI”, Magn. Reson. Med. 2002;47:42-52; Forbes K P, Pipe, J G, Karis J P, Heisemian J E, “ImprovedImage Quality and Detection Of Acute Cerebral Infarction With PROPELLERDiffusion-Weighted MR Imaging, Radiology 2002; 225:551-555; Forbes K P,Pipe, J G, Karis J P, Farthing V, Heiserman J E, “Brain Imaging In theUnsedated Pediatric Patient: Comparison Of Periodically RotatedOverlapping Parallel Lines With Enhanced Reconstruction and Single-ShotFast Spin-Echo Sequences”, AJNR Am J Neuroradiol 2003; 24:794-798. Thismethod uses multishot FSE acquisitions incorporated with a k-spacetrajectory somewhat similar to that used in the projectionreconstruction method.

Irrespective of the sampling method used, after k-space data isacquired, images are reconstructed from the acquired k-space data bytransforming the k-space data set to an image space data set. There aremany different methods for performing this task and the method used isoften determined by the technique used to acquire the k-space data. Witha Cartesian grid of k-space data that results from a 2D or 3D spin-warpacquisition, for example, the most common reconstruction method used isan inverse Fourier transformation (“2DFT” or “3DFT”) along each of the 2or 3 axes of the data set. With a radial k-space data set and itsvariations, the most common reconstruction method includes “re-gridding”or “re-binning” the k-space samples to create a Cartesian grid ofk-space samples and then perform a 2DFT or 3DFT on the re-binned k-spacedata set. In the alternative, a radial k-space data set can also betransformed to Radon space by performing a 1DFT of each radialprojection view and then transforming the Radon space data set to imagespace by performing a filtered backprojection.

As discussed above, collected k-space data is a subset of the “rawdata”, which is stored in a file called a raw data file in the datastorage of an MR imaging system, usually in volatile memory. The formatof each raw data file is vendor, and in some cases, scanner-typespecific. However, raw data files usually include two parts, a headerand a body. The header provides an identifier to the data or gives afile name for the data or provides ancillary non-image information, andtypically contains patient information and pulse sequence information,including time of acquisition. The body contains the actual raw datathat has been collected during the pulse sequence, usually in achronological order. Typically, a pulse sequence obtains informationfrom multiple slices of the body. The raw data is not slice specific andcontains data from all slices selected prior to running the pulsesequence. The raw data in the data section is often maintained or storedin volatile memory in a sequence that is in the order of dataacquisition.

After image reconstruction, the reconstructed image is stored in an MRIimage file, which can be stored either locally, or in a Picture ArchiveCommunication System (PACS). MR image files are usually in avendor-independent format called DICOM. Using the DICOM format, each MRimage file has a header portion and a body portion. The header portioncontains information similar to that located in the raw data header aswell as information about the specific corresponding imaging slice, e.g.image slice number. The body portion contains the actual image dataTypically, each MR image file contains image data about one imagingslice.

Although the MR image file is retained, the “raw files” including theunderlying k-space data are not retained, or at best are retained onlyin the vendor-specific format. Therefore, in these prior art methods,the underlying data acquired during the scan is lost, and if additionalviews or visualization is required beyond the reconstructed scans,additional scans must be performed, and new sets of data must beacquired. It would be desirable, therefore, to retain a representationof the underlying k-space data and the associated identifying data toallow for additional images to produced after the data is acquired.

SUMMARY OF THE INVENTION

The present invention is a method for storing image data forreconstruction of MRI images. The method comprises the steps ofacquiring subsets of k-space data through magnetic resonance imaging,optionally filtering said data, performing an inverse Fourier transformof said data to form all intermediate image, saving the intermediateimages in a standard format, and repeating steps a through c until thesubsets of k-space data for an entire scan is stored in a correspondingintermediate images. The saved intermediate images are stored in astorage medium wherein the images can be retrieved and summed toreconstruct the image of the scan.

In another aspect of the invention, a method for post-acquisitionreconstruction of an image from magnetic resonance data using isdisclosed. In this method, k-space data is acquired through magneticresonance imaging. An inverse Fourier transform on selected portions ofthe k-space data to construct corresponding intermediate images, and theintermediate images are stored in a data storage medium. A plurality ofintermediate images are retrieved from storage and a selected view isconstructed by summing the plurality of intermediate images.

In a particular embodiment, the step of acquiring a portion of k-spacedata comprises performing a radial acquisition of k-space data atcontinuously incrementing angles over a predetermined time, andcollecting a corresponding portion of k-space data for each radial armof k-space data is per RF excitation during a selected time interval.From this data a high resolution spatial image can be reconstructed, aswell as high temporal resolution images, providing a base spatialreconstruction for comparison to changes over time, particularly as acontrast agent is administered.

This invention therefore provides a system for reconstruction of MRIimages after data acquisition. There are three key features of thissystem. First, intermediate images corresponding to sub-sets of k-spacedata acquired during data acquisition are stored on an image storagesystem. Second, reconstruction is performed at later time using simplesummation of corresponding intermediate image data to create finalimages. Third, this final image reconstruction can be performed fromthese stored intermediate images based on user-defined anduser-modifiable parameters such as spatial and temporal resolution, e.g.adaptive reconstruction.

This invention also provides an imaging system for delayed finalreconstruction of images after data acquisition. The basic principle ofthis imaging system is (1) a division of the standard multi-step imagereconstruction using intermediate images reconstructed from sub-sets ofcollected data (2) storage of intermediate image files, and. (3)reconstruction of final images (completed reconstruction) fromintermediate images (partially reconstructed data) at a later time. Apreferred embodiment in MRI would be an imaging system for use withradial techniques of k-space acquisition and an adaptive reconstructioninterface,

In the case of MRI, conceptually, this system takes advantage of themathematical equivalence of a partially reconstructed image (for whichan Inverse Fourier Transform has been applied), and the correspondingsub-set of k-space data (highlighting the relationship of the imagedomain versus the k-space domain). A fill set of collected k-space datais equal to the sum of sub-sets of -space data. The Inverse FourierTransform of the full set of collected k-space data is also equal to thesum of the Inverse Fourier Transforms of sub-sets of the collected kspace data. Thus, the result of a delayed reconstruction of a finalimage by summation of the intermediate images is equivalent to a fullinitial reconstruction of k-space data into one single final image. Thedelay in reconstruction permits the use of intermediate image storagemediums such as PACS and is easily amenable to adaptive reconstructionwith a user interface for real-time or neat-real-time adjustment.

There are numerous characteristics of the present technology. First,image reconstruction may be performed on sub-sets of acquired dataduring the time of initial data acquisition. The resulting images arecalled intermediate images. In the case of MRI, sub-sets of k-space dataarc used. Also with regard to MRI, raw data files are no longer usedafter this point and standard image filtration is not necessarilyperformed at this point. With any modality, the original data is notnecessarily required for later use as partial image reconstruction hasbeen performed.

Second, the resulting intermediate images may be stored on an imagestorage system. In medical imaging, this storage system may be a PictureArchiving Communications System (PACS) and the intermediate image formatmay be in a standard vendor-independent format such as DICOM. PACSsystems are available in most clinical settings.

Third, final image reconstruction is performed at later time, nominallyusing simple summation of corresponding image data, although otheralgorithms may be used, e.g. adaptive filtering in the image orfrequency domain.

Fourth, in one MRI form of this invention, an adaptive reconstructiontechnique may be used. Final image reconstruction can be performed fromthe stored intermediate images based on user-defined and user-modifiableparameters such as spatial and temporal resolution. Real-time ornear-real-time parameter variation and final image reconstruction can beperformed by the user if desired.

Additionally, this invention may be used for other forms of medicalimaging, such as multi-detector computed tomography (MDCT).

These and other aspects of the invention will become apparent from thefollowing description. In the description, reference is made to theaccompanying drawings which form a part hereof, and in which there isshown a preferred embodiment of the invention. Such embodiment does notnecessarily represent the fall scope of the invention and reference ismade therefore, to the claims herein for interpreting the scope of theinvention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an MU system which employs the presentinvention;

FIG. 2 is a flow chart illustrating the adaptive reconstruction processof the present invention; and

FIG. 3 is a flow chart illustrating a specific application of theadaptive reconstruction process of the present invention,

GENERAL DESCRIPTION OF THE INVENTION

Adaptive reconstruction describes a method of reconstructing final imagedata from acquired raw data that has information to create both highspatial resolution low temporal images as well as lower spatialresolution high temporal images. For instance, a pulse sequence mayacquire data continuously in a projection reconstruction manner over aperiod of minutes, e.g., 340 minutes, such as during breast MRI, afteran intravenous injection of contrast. Traditionally, final images arecreated by combining k-space data from serial acquisitions over a longtime period in order to create high spatial resolution images, or over ashort time period to create high temporal resolution images. In adaptivereconstruction, raw data is transformed into intermediate images(imagewise organized multiple pixels, partial digital lines, completedigital lines, groups of digital lines, etc., but less than an entireframe of an image). These intermediate images are stored. An operatordetermines or commands specific or general parameters that are to beused in the reconstruction of an image (e.g., spatial resolution,temporal resolution, contrast factors, etc.), and intermediate imagedata is reconstructed according to the commanded parameters to form afinal image. The manner of data flow and reconstruction is along thestandard technique of completing k-space acquisition prior to inverseFourier Transform (image reconstruction) and image filtering.

A method of post-acquisition reconstruction of an image from magneticresonance data using an intermediate step of partially reconstructedimages according to the present technology includes:

a. providing partial k-space data through magnetic resonance imaging;

b. performing Fourier reconstruction of the partial k-space data intointermediate images

c. storing the intermediate images in a data storage media

d. retrieving a multiplicity of stored intermediate images andconstructing a final viewable image by aggregating the multiplicity ofstored intermediate images.

The constructing, reconstructing and aggregating of partial k-space dataand intermediate images data is done along rational imagingmethodologies. Data on particular areas are combined with data from thesame particular area, data in time sequences are combined in an orderconsistent with the sequence of time events, data with image effectsfrom patient movement may be sharpened before being used in theconstruction or aggregation of data, and the like. Available algorithmsfor combining the data are known, and by retaining the intermediateimages, specialized images of particularly desired areas and/orparticularly desired time sequences can be constructed to provide aviewable image that was not an actual image taken by an actual scan bythe MRI. The reconstructed image (actually an originally constructedvirtual image from real data) may be configured along plane lines otherthan those of the actual scans (by taking data points from among aseries of adjacent planar scans so that a plane of data, or approximateplane of data is created) so that an image can be originally constructedalong a selected data plane for which no actual direct image was taken.For example, if the plane of original slice imaging was essentiallyperpendicular to the spine of a patient, and if an image is desired thatshows a particular element (e.g., a duct, vein, artery, wound, scar,etc.) that exists in a different angular orientation than the slices, avirtual plane can be constructed through the intermediate images, alldata points along the intersection of the plane with the intermediateimages aggregated, and any missing data between points established byinterpolation between the points on adjacent planes Where actualintermediate images have been created. The angle of the plane would beselected to have the particular element lie within the virtual plane.

This invention describes a system for the capture of digital image data,transformation of captured digital image data into intermediate imagedata files, storage of the intermediate image data files andreconstruction of final MRI images from the intermediate image datafiles after data acquisition and storage. It has a unique data flow andmethodology (FIG. 2) that is distinct from the prior art (FIG. 1). Anadditional key concept is the use of the understanding that the sum oftwo image data sets is equivalent to the sum of the corresponding twok-space data sets. All data addition (e,g., referring to thereconstruction of the final image from the intermediate image data) inthe proposed concept is performed in the image domain rather than in thek-space domain. This feature provides salient advantages for a uniqueform of adaptive reconstruction and visualization sa described in thefollowing sections,

A. Partial Data Set Acquisition

Sub-sets of k-space data are initially reconstructed into images, whichcan be called intermediate images. The sub-set of k-space data is highlyvariable. Nominally, it may be a single point of k-space data. It can bea line of k-space data in a Kumar-Welti-Ernst acquisition or projectionreconstruction acquisition. The intermediate image reconstructioninvolves an Inverse Fourier Transform. Filters may be applied bymultiplication prior to reconstruction. A time-stamp based on the timeof acquisition of the data is placed in the DICOM image header section.

B. Image Storage

The resulting intermediate images are then saved on an image storagesystem or medium. Typically, the intermediate images are maintained inDICOM format. This can be something portable like a CD or DVD or can bemore complicated as in PACS network. The intermediate images can besaved for an indefinite period of time.

It is desirable at this stage of the description of the technology toappreciate the nature of the intermediate images and intermediate imagedata. The original imaging system provides raw data which can beconsidered to correspond to either “spots” (single point data signals)or pixels (the smallest element of image content, which in some otherimaging systems formed from combinations of spots). These raw dataelements also carry identifiers so that specific raw data elements canbe retrieved or operated upon in a meaningful manner by operation ofsoftware. Because of the total volume of data content needed inprovision of data for an entire image in raw data form, raw data isusually retained in volatile memory or limited local disk storage withautomated first-in-first-out deletion so that storage space for theentire image data content can be reduced. Additionally, generation of animage from raw data every time an image is desired would use significantcomputing power and increase the time in which the final image can bedisplayed. This is one reason by complete images are stored, with asingle image identifier (as in DICOM) with image data information alsoassociated with the file for the complete final image. However, storageof final images limits the utility of the images and, within specificimaging, display and storage systems, may limit the ability of images tobe conveyed or used by other imaging, display and storage systems,mainly because of unique and proprietary software. Once reconstructionhas occurred and the raw data is lost, no other image reconstructionsmay be performed.

According to the present proprietary technology, raw data is converted(e.g., by Fourier transform, partial Fourier transform, or half-Fouriertransform, etc.) into a common or universal language file (e.g., DICOM,. . . , etc.). The transformation is not done into a complete and finalimage that would effectively complete a frame of an image. Rather, theadaptive reconstruction system defines at least one range of data (e.g.,pixels or spots from raw data) that is less than an entire final image(e.g., less than 5% of the image data content in a final image frame,preferably less than 2%, more preferably less than 1% of the image datacontent in a final image frame, and even less than 0.1% or less than0.01% of the total image data), such as a single scan or image line ofdata from the image or a partial scan line (e.g., the first 25%, thesecond 25%, etc.), It is also possible to store line segments (e.g., 5%,10%, 25%, 50% of an entire line) or even pixel groupings (e.g., matricesof 16,64, 100, 144,200, etc. squares within a matrix). These segments orIttupings become the intermediate images or the intemiediate image data,and each intermediate image or intermediate image data⁻file containsidentity information so that it can be organized when stored, retrievedin a sensible fashion and reconstructed into a final image. Thesesegments or groupings (the term segment hereinafter will be used tocollectively describe the intermediate images, whether in line or pixelmatrix format) are stored, as in DICOM or other common or universalsoftware language or format, and because the intermediate images areidentified (e,g., in the identity data or header section), theintermediate image files are in a retrievable format for reconstructioninto a designed image, adaptively reconstructed image, or reconstructedcomplete final image.

The selection of the parameters for reconstruction of an image (the termreconstruction is used here generically to include adaptivereconstruction and total image reconstruction) can be input by theoperator either by specific identification of individual parameters orby selecting a hyper-program that automatically operates on the basedata to provide an image with predefined parameters. For example, if thesystem is capable of providing a high resolution image (using ascholastic scale of 10 as the highest resolution), automated programsmay be available for selecting an image with a resolution on thescholastic scale of, 10,9, 8,7, 6, 5, etc. at the selection of theoperator. Similarly, if the data base has images with a temporaldistribution over 10 minutes, there may be an automated program forproviding images from 1 minute, 4 minutes and 10 minutes, and this maybe done in combination with a scholastic scale image resolutionparameters. This offers significant flexibility and speed top thesystem, as in the following manner. Higher temporal resolution can beobtained by user-selection of image reconstruction from data from 30second intervals or 60 second intervals. This can be variable accordingto user requirements in looking at the image data.

It is often desirable to rapidly move through a series of availableimages to look for specific image features or events that have beenimaged. By selecting lower temporal resolution images, the operator canrapidly move through larger numbers of images (they can be retrieved anddisplayed faster because of the lower temporal resolution (which may beeffected by a number of techniques later described herein), and when theoperator sees a feature or event of interest, higher spatial resolutionon the scholastic scale or shorter time intervals may be selected by theoperator and viewed in greater detail,

Simple methods of altering the resolution of an image are reduction ofthe limber of total available lines used in the displayed image, and theuse of smoothing functions to combine those fewer lines into a viewableimage. For example, if the total number of available lines for a totalimage is 1024 lines, a scholastic scale of 9 might use only 960 lines(1024−64 lines, or 1/16 of the total number of lines) and a program tosmooth the image where the lines are missing. Similarly a scholasticreading of 8 might be 902 or 896 lines (approximately 1/10 of thelines), a scholastic reading of might be 832 lines, and a scholasticrating of 5 might be 524 lines. The smoothing function would make the 15removal of lines less obvious by merging data between adjacent lines.

More complex usage of the intermediate images may also be used in actualimage search functions. For example, if a total and complete image has anearly circular object within the image (e.g., a small tamer or cyst), asearch function may be provided to look for adjacent lines or groups ofpixel matrices that have the specific data content that would beindicative of a circular object. Such data content would be that aseries of at least X adjacent lines have contrast data along theadjacent lines that increase in a fairly uniform rate (consistent withthe increasing segment length of a circle) and then a next series of Yadjacent lines that have contrast data along the Y adjacent lines thatdisplay decreasing segment length (consistent with the decreasingsegment length of a circle). When these data events are found in ascreening of the base data for a single image frame, either that singletotal image can be displayed or only that segment (and a framing areafor convenience) will be displayed. This enables an automated search ofimages by considering only intermediate image data (rather than entireimages), and even by selecting intermediate image data in specificregions of the image frame (e,g., the central 50% of the image where thetargeted area of interest is present).

Applicants use the term aggregating intermediate image data oraggravating intermediate images as a term to exclude the totalreconstruction of an image from intermediate image data. In the totalreconstruction of an image, essentially 100% of the base data would beused in the reconstruction, or at least all base data in the image wouldbe originally contiguous scans or lines of data with no intermediatescans or lines left out of the image. The term aggregation means thatless than 100% of the lines of data available for an image are used inreconstructing a final or intermediate image, and specifically that atleast some originally adjacent lines of image from the entire mass ofdata (or adjacent pixels) are not used in the partial or adaptivereconstruction used in the practice of the present technology.Particularly where there are at least some adjacent lines of image datafrom within the central regions of the image that are missing, theconstruction of the image is less amenable to the term reconstruction(which implies return to the original form) and the term aggregationimplies a construction with fewer than all image data in the final orintermediate image. By emphasizing adjacent lines (lines of data thatare available adjacent to at least two lines used in the construction ofthe image, or at least a line that would otherwise be intermediate twolines of data used in the constructed image, as where two or more linesthat would be adjacent have been removed), the removal of data can beclearly distinguished from cropping wherein entire sections surroundingother images are removed.

C. Delayed Image Reconstruction

Reconstruction of final images according to the technology of thepresent disclosure is then performed at a later time, the reconstructionperformed from an appropriate selection of intermediate image data. Noraw data is used. The reconstruction can be performed on anotherworkstation or remotely. Because image data is usually in a commonformat such as DICOM, this process is vendor independent, acquisitionindependent, and MR scanner independent in this manner. This isdifferent from raw data, which is typically vendor specific andsometimes MR scanner type specific.

In this description, the steps for reconstruction are: (1) selection ofappropriate intermediate images, (2) summation of the appropriateintermediate images. What is meant by the term “appropriate” relates tothe selection of intermediate images that are related in space and timeso that information (including image data) further developed from theintermediate images can be related, integrated or imagewise associatedin a meaningful event. Thus, appropriate would include spatiallyadjacent points, spatially adjacent lines, spatially adjacentperspective lines, spatially adjacent planes, and such intermediateimages also related in a temporal sense, such as sequences of the samespatially adjacent lines over measured or predetermined time intervals.Contrariwise, an inappropriate set of images for summation would includea single point or line from a cranial MRI intermediate image and asingle point or slice from an MRI thoracic intermediate image.

In essence, the described technique can be applicable to k-spaceacquisitions whereby each single acquired k-space data point isconverted into a corresponding intermediate image which is stored forlater retrieval for summation into a final image. In practice, a line ofk-space acquisition data points would be converted into a correspondingintermediate image as a matter of efficiency.

D. Adaptive Image Reconstruction Controls

The selection of appropriate intermediate images can be performedwithout or with the use of reconstruction. In the nominal case, noadaptive reconstruction is used. For example, a pulse sequence with 256phase encodes may have been reconstructed initially into 256intermediate images, each created from a single line of k-space data.These 256 intermediate images are then stored for later reconstruction.At a later time, these intermediate images are reconstructed bysummation and displayed as a final image. In certain circumstances, itmay be advantageous to have adaptive reconstruction. Adaptivereconstruction involves reconstruction that is responsive to the user'sdesired imaging parameters, which may be changed in real-time. Forinstance, a pulse sequence may acquire data continuously in a projectionreconstruction manner over a period of minutes, e.g., 6 minutes, such asduring breast MRI, after an intravenous injection of contrast.

With the technique described in this invention, each line of theprojection reconstruction acquisition is converted into a correspondingintermediate image. The resulting intermediate images are then storedfor later reconstruction or with immediate reconstruction in real timeor built up in sequence as adaptive reconstruction. The time-stampinformation in the image headers is used for determining the order ofacquisition of data. The flexibility and other advantages of vendor andMR scanner independent reconstruction are maintained because of the useof an intermediary image format such as DICOM.

An additional aspect of this invention is a user interface that permitsa user to directly determine the spatial and temporal resolution offinal images to be created. After such selection, final images may becreated by the summation of an appropriate number and set ofintermediate images.

For example, it may be desired to have both a high spatial resolutionfinal image as well as a set of temporally related lower spatialresolution final images. A high spatial resolution final image may becreated by a large number of serial intermediate images eachrepresenting a single line of projection reconstruction acquisition.Assume that TPRL is the time to acquire one projection reconstructionline. If 128 intermediate images arc required for such an image, itrepresents a temporal resolution of 128×TPRL.

A series of lower spatial resolution final images with a higher temporalresolution may be obtained, by using a smaller number of intermediateimages that are temporally related to each other. For example, 16projection reconstruction lines may be used to create a lower spatialresolution image with a higher temporal resolution of 16×TPK. Thisseries of final images may then be used for dynamic contrast enhancementevaluation. By storing the individual intermediate images, an operatorcan select parameters from among spatial image resolution and temporalimage resolution to design or tailor an image into a format having thespecific image and temporal properties desired at that time. Inscreening a complex set of images, the speed of image generation fromsuch stored intermediate images can be increased, and then uponidentifying an image or field of particular interest, the parameters canbe reconfigured and a more exacting image (e.g., greater spatialresolution, narrower temporal resolution) can be generated according tothe design intent of the operator.

The user interface permits the real-time adjustment of temporal andspatial resolution of resulting images. This allows for rapid visualfeedback For instance, on creating final dynamic contrast enhancementimages (high temporal resolution, low spatial resolution), visualacceptance or rejection of the compromise in spatial resolution thatcomes with high temporal resolution can be made rapidly by simplychanging parameters with the interface, e.g. sliders or dials.Similarly, there can be rapid visual feedback for an acceptable highspatial resolution final image (at the cost of temporal resolution whichmay increase blur).

Moreover, there can be a combined approach whereby low-spatialresolution, high temporal resolution images are used to find a temporalpoint of optimal enhancement or tissue contrast followed by ahigh,spatial resolution reconstruction centered in that particularregion and temporal point can be made. Alternatively, once a region ofinterest is being examined in a high spatial resolution image, it may bedesired to then create localized high temporal resolution enhancementimages in the selected region for real-time characterization,

DESCRIPTION OF THE PREFERRED EMBODIMENT

Referring particularly to FIG. 1, the preferred embodiment of theinvention is employed in an MRI system such as the one shown here by wayof example. The MRI system includes a workstation 10 having a display 12and a keyboard 14. The workstation 10 includes a processor 16 which is acommercially available programmable machine running a commerciallyavailable operating system. The workstation 10 provides the operatorinterface which enables scan prescriptions to be entered into the KMsystem. The workstation 10 is coupled to four servers: a pulse sequenceserver 18; a data acquisition server 20; a data processing server 22,and a data store server 23. The workstation 10 and each server 18, 20,22 and 23 are connected to communicate with each other,

The pulse sequence server 18 functions in response to instructionsdownloaded from the workstation 10 to operate a gradient system 24 andan RF system 26. Gradient waveforms necessary to perform the prescribedscan are produced and applied to the gradient system 24 which excitesgradient coils in an assembly 28 to produce the magnetic field gradientsGx, Gy and Gz used for position encoding MR signals. The gradient coilassembly 28 forms part of a magnet assembly 30 which includes apolarizing magnet 32 and a whole-body RF coil 34.

RF excitation waveforms are applied to the RF coil 34 by the RF system26 to perform the prescribed magnetic resonance pulse sequence.Responsive MR signals detected by the RF coil 34 are received by the RFsystem 26, amplified, demodulated, filtered and digitized underdirection of commands produced by the pulse sequence server 18. The RFsystem 26 includes an RF transmitter for producing a wide variety of RFpulses used in MR pulse sequences. The RF transmitter is responsive tothe scan prescription and direction from the pulse sequence server 11 toproduce RF pulses of the desired frequency, phase and pulse amplitudewaveform.

The RF system 26 also includes one or more RF receiver channels. Each RFreceiver channel includes an RF amplifier that amplifies the MR signalreceived by the coil to which it is connected and a detector whichdetects and digitizes the I and Q quadrature components of the receivedMR signal. The magnitude of the received MR signal may thus bedetermined at any sampled point by the square root of the sum of thesquares of the I and Q components:

M=√{square root over (I ² +Q ²)},

and the phase of the received MR signal may also be determined:

φ=tan⁻¹ Q/I.

The pulse sequence server 18 also optionally receives patient data froma physiological acquisition controller 36. The controller 36 receivessignals from a number of different sensors connected to the patient,such as ECG signals from electrodes or respiratory signals from abellows. Such signals are typically used by the pulse sequence server 18to synchronize, or “gate”, the performance of the scan with thesubject's respiration or heart heat.

The pulse sequence server 18 also connects to a scan room interfacecircuit 38 which receives signals from various sensors associated withthe condition of the patient and the magnet system. It is also throughthe scan room interface circuit 38 that a patient positioning system 40receives commands to move the patient to desired positions during thescan.

The digitized MR signal samples produced by the RF system 26 arereceived by the data acquisition server 20. The data acquisition server20 operates in response to instructions downloaded from the workstation10 to receive the real-time MR data and provide buffer storage such thatno data is lost by data overrun. The data processing server 22 receivesMR data from the data acquisition server 20, and stores the data in araw data file. The format of the raw data We is typically specific tothe MRI system vendor, or to a specific system. The raw data filetypically includes a header identifying This data file, moreover, istypically temporary, and is deleted when the system is deactivated.

The data is processed in accordance with instructions downloaded fromthe workstation 10. Such processing may include, for example: Fouriertransformation of raw k-space MR data to produce two orthree-dimensional images; the application of filters to a, reconstructedimage; the performance of a backprojection image reconstruction ofacquired MR data; the calculation of functional MR images; thecalculation of motion or flow images, etc.

In a typical system, individual scan lines are combined to form data fora complete scan image, which is reconstructed by the data processingserver 22, and conveyed back to the workstation 10 where the image canbe stored with other images. Real-time images are stored in a dataintermediate memory cache (not shown) from which they may be output tooperator display 12 or a display which is located near the magnetassembly 30 for use by attending physicians. Batch mode images orselected real time images are stored in a host database on disc storage44, such as a picture archiving and communicating system (PACS). Whensuch images have been reconstructed and transferred to storage, the dataprocessing saver 22 notifies the data store server 23 on the workstation10. The workstation 10 may be used by an operator to archive the imagesin the PACS system., produce films, or send the images via a network toother facilities. Typically, these reconstructed images are stored in astandard, vendor-independent format such as the Digital Imaging andCommunications in Medicine (DICOM) standard.

Referring now to FIG. 2, a flow chart illustrating the method of thepresent invention is shown. Here, in process step 50, a patient isplaced in an MRI scanner. Subsequently, in process step 52, pulsesequences are applied and consecutive slice images are taken inindividual scan lines. These images can be taken using any of the pulsesequences described above. Data acquired from these individual slices orscan lines is, as described above, stored in raw data files in dataacquisition server 20.

As or after the raw data is acquired, in process step 53, the data isfiltered, and in process step 54, an inverse Fourier transform isapplied to sub-sets of the k-space data to reconstruct partial images,which will be referred to herein as intermediate images. This imagereconstruction step can be applied to a single point of k-space data anindividual line of k-space data from a radially symmetric acquisitionscheme such as projection reconstruction or spiral acquisition; acollection of k-space lines such as, for example, a “blade” or“propeller” from a PROPELLER pulse sequence; a single line of k-spacedata from a rectilinear acquisition such as a Kumar-Welti-Ernstacquisition; a line segment, or a portion of a complete line; a pixelgrouping; or to other sub-sets of combined k-space data. Although afiltering step is described prior to the transform step of process step54, in an alternative construction, the data can be filtered byapplication of convulation to the intermediate image.

In process step 56, the intermediate images are stored in a standardizedformat, such as the DICOM format discussed above, and are preferablymarked with a time stamp, which can be an actual time of acquisition or,if multiple lines have been acquired, an effective time of dataacquisition, such as the mean time of data collection or the start timeof a data acquisition. These files can be stored in the workstation 10,and are advantageously stored in an associated PACS system 44, oralternatively to a portable memory storage such as a DVD, a CD, flashdrive, portable static RAM, or other medium.

Because an intermediate image is mathematically equivalent to thecorresponding sub-set of k-space data (highlighting the relationship ofthe image domain versus the k-space domain), a full set of collectedk-space data is equal to the sum of sub-sets of k-space data, and theinverse Fourier transform of the full set of collected k-space data isalso equal to the sum of the inverse Fourier transforms of sub-sets ofthe collected k-space data. Thus, the stored intermediate images from agiven can be retrieved from memory, and, in process step 58, summedtogether to reconstruct selected images from the intermediate images.The resultant reconstructed image can be the equivalent to a fullinitial reconstruction of k-space data into one single final image, orselective portions of the intermediate images can be summed to meet thespecific temporal and spatial requirements of the user.

Because the intermediate images can be retrieved from the PACS system44, reconstruction of images can be performed from workstations remotefrom the MR! system. Thus, for example, the images can be reconstructedat a separate workstation, via an interface to the PACS system, througha network associated with the PACS system, such as from a thin clientgateway to a thick server, or from other remote locations at which theintermediate images are available.

All of the data acquired in a scan can be used to reconstruct the finalimage. However, in one form of the invention, an adaptive reconstructioncan be provided in which subsets of the acquired data are combined toprovide a spatial and temporal resolution desired by the user.Therefore, the data acquired from a scan can be manipulated to providean improved visualization, without the need for additional scanning.

The method of the present invention is useful in all types of MRIexaminations, and is particularly useful in the field of breast imaging.Breast MRI protocols typically include a high spatial resolutionexamination and a dynamic contrast enhanced examination. In the latterexamination, intravenous contrast is administered and the pattern ofenhancement of various breast lesions is observed MT a 5-6 minuteinterval. One pulse sequence that can be used, for example, is a 1minute long 3D spoiled gradient echo sequence. This is concatenated overthe 5-6 minute interval to create 5-6 3D image data sets representingserial images of contrast enhancement at one minute intervals. The 1minute intervals are required for each acquisition because high spatialresolution is desired. Longer acquisitions are not desired as this wouldcompromise the time interval of assessment of contrast enhancement

Referring now to FIG. 3, similar results can be achieved using anadaptive reconstruction, as described below. Here, in one embodiment ofthe invention, a patient is placed in a scanner (process step 62) and aradial acquisition of k-space data (one radial arm per RF excitation) isperformed at continuously incrementing angles over a predetermined time,preferably during contrast enhancement of the target organ (process step60). Preferably, one radial arm of k-space data is collected per RFexcitation during the total time interval of contrast enhancement(process step 64). The radial acquisition is preferably a PROPELLERacquisition, but can also be a spiral k-space acquisition, or other scantypes which result in a continuous acquisition of radially oriented armsduring a time period. The order of acquisition of the radially orientedanus is variable, and may rotate clockwise or counterclockwise. Ineither case, the blades are continually acquired, preferably over a 6minute period, although other time periods can also be used. At the endof each acquisition of a blade, the data is filtered (process step 65),an inverse Fourier transform is performed on the corresponding k-spacedata (process step 66), and a corresponding intermediate image isobtained, as described above. These images are saved in a DICOM format,and can be stored in a PACS system 44 or other memory storage, also asdescribed above. Although a filtering step is described as taken beforethe inverse Fourier transform, in an alternative method, convulation canbe applied to the intermediate images before or after retrieval by aworkstation or other external system.

After the original data acquisition, in process step 68, a user canre-construct images from the stored intermediate image data. Theacquired intermediate images are downloaded from the memory storageDICOM format, and a user can interactively select a desired spatialresolution, temporal, resolution, and interval of image reconstruction.Corresponding images are then reconstructed by appropriate summation ofthe intermediate image data from the acquisition.

As described above, for a proper analysis of the breast, thereconstructed images preferably include one or more high spatialresolution image data set(s), either two or three dimensional, which arecreated by combining many intermediate images together with associatedreduction in temporal resolution, e.g. the images have a high spatialresolution and acquisition is performed over a limited period of time,thereby providing a high resolution intermediate image for comparisonpurposes, addition, a series of high temporal resolution image data sets(again, either in two or three dimensions) is created by combining areduced number of intermediate images at stepped time points during theentire dynamic acquisition, thereby providing a series of images thatprovide changes over time as the contrast enhancement agent is applied,and which can be compared to the high resolution intermediate image.

Because image data rather than raw data is stored in this system, andbecause reconstruction is performed on MR image files (DICOM format)rather than raw data files, the process is vendor independent,acquisition independent, and MR scanner independent

As are example, during the acquisition, for a desired spatialresolution, 90 arms of a radially oriented acquisition can be acquired,with the effective time of acquisition equal to the desired time pointof acquisition, and with a time separation between reconstructed imagesequal to a desired temporal interval. Because the 90 images arecollected over 90 TR intervals, this represents a temporal resolution ofthe 90×TR. If a higher temporal resolution is desired, this typicallycauses a decrease in spatial resolution.

For high spatial resolution, a large number of serially collected radialarms are combined to form a k-space data set for image reconstruction.The temporal resolution of the collected data is reduced as largernumber of radial arms corresponds to a longer time period of datacollection per desired imaging slice.

Similarly, for high temporal resolution, a smaller number of seriallycollected radial arms can be combined to form a k-space data set forimage reconstruction. Spatial resolution, however, is reduced due to thereduced number of radial arms that are collected, leading to reducedsampling of the desired k-space volume.

Although the invention is described above with respect to MR imaging,the present invention can also be used with computed tomography (CT)scanning. X-ray CT involves the use of an X-ray source and the detectionof transmitted X-rays on the other side of the object to be imaged(usually a patient) nominally with a row of X-ray detectors, or usingmultiple parallel rows of detectors in a technique called multi-detectorCT or MDCT. Typically, the traditional method used for creation of X-raycomputed tomography (CT) images is known as filtered back projection. Inthe 2D case, detector data from multiple projections of X-rays throughan object is re-binned into parallel beams of X-rays. Each re-binnedcollection represents a “projection” or “view” through the object.Mathematically, each projection represents a single line of data in theFourier representation of the final image, and therefore is similar toprojection reconstruction MR techniques.

In filtered back-projection, data from each view is filtered thenprojected through the image space, summing on top of data from otherprojected views. This is mathematically the equivalent of summingprojection reconstruction lines in the Fourier space and filtering thefinal data prior to Inverse Fourier Transform. The 3D case is simply amore complex version of filtered back projection or this Fourier pathwayequivalent.

In the ease of computed tomography imaging, a patient is initiallypositioned in a scanner, and detector data from multiple projections ofX-rays are acquired and re-binned into parallel beams of X-rays. AFourier transform is performed on each projection, and the resultantimage is filtered by multiplication to provide intermediate image& Theseintermediate images can them be summed together to provide images ofvarying spatial and temporal resolution, as discussed above.

It should be understood that the methods and apparatuses described aboveare only exemplary and do not limit the scope of the invention, and thatvarious modifications could be made by those skilled in the art thatwould fall under the scope of the invention. To apprise the public ofthe scope of this invention, the following claims are made:

1. (canceled)
 2. A method for displaying image data from a medical imagescanner using a computer system having a display, the method comprisingacts of: presenting, to a user in the display of the computer, a userinterface; receiving, via the user interface, a selection ofuser-defined parameters for constructing a reconstructed image;receiving and saving a plurality of intermediate images in a standardformat, the plurality of intermediate images acquired from the medicalimage scanner; and retrieving and reconstructing the reconstructed imageby aggregating a set of the plurality of the intermediate images, theset corresponding to the user-defined parameters.
 3. The method of claim2, further comprising acts of: acquiring k-space data through magneticresonance (MR) imaging; performing an inverse Fourier transform on theacquired k-space data of to form an intermediate image; saving theintermediate image in the standard format; and iteratively performingthe steps of acquiring, performing, and saving until the pluralityintermediate images are stored for the acquired k-space data.
 4. Themethod of claim 2, further comprising acts of: performing a ComputedTomography (CT) medical imaging scan on an area of interest of apatient; acquiring a plurality of projections from the CT medicalimaging scan; filtering each of the plurality of projections; andperforming a Fourier transform on each of the projections to form theplurality of intermediate images.
 5. The method of claim 2, furthercomprising an act of displaying, via the display, the reconstructedimage conforming to the user-defined parameters.
 6. The method of claim2, wherein the act of receiving the selection of user-defined parametersfurther includes an act of receiving the selection of at least one ofspatial resolution parameters and temporal resolution parameters.
 7. Themethod of claim 6, further including acts of: receiving anotherselection of user-defined parameters having one of adjusted spatialresolution parameters or adjusted temporal resolution parameters; andretrieving and reconstructing the reconstructed image by aggregatinganother set of the plurality of the intermediate images, the another setcorresponding to the another selection.
 8. The method of claim 2,wherein the act of receiving the selection of user-defined parametersfurther includes an act of receiving the selection of at least one of aspatial distribution and a temporal distribution parameters.
 9. Themethod of claim 8, wherein the act of receiving the selection ofuser-defined parameters further includes an act of receiving theparameters for a hyper-program, and the method further comprisesanalyzing the plurality intermediate images to retrieve and reconstructan image having one of a resolution on a scholastic scale or a temporalrange, associated with the plurality of intermediate images.
 10. Themethod of claim 2, further comprising an act of receiving, via the userinterface, a search selection for selectively searching the intermediateimages for data having specific data content.
 11. The method of claim 2,further comprising receiving, via the user interface, a selectionidentifying a field of interest within the reconstructed image; andretrieving and reconstructing the reconstructed image by aggregatinganother set of the plurality of the intermediate images, the another setcorresponding to the selection identifying the field of interest.
 12. Asystem for displaying image data for reconstruction of images from amedical image scanner, the system comprising: a user interfaceconfigured to be presented, to a user in a display, and configured toreceive a selection of user-defined parameters for constructing areconstructed image; a processor, coupled to the user interface, andconfigured to: receive and save a plurality of intermediate images in astandard format, the plurality of intermediate images are acquired fromthe medical image scanner; and retrieve and reconstruct thereconstructed image by aggregating a set of the plurality of theintermediate images, the set corresponding to the user-definedparameters; and a storage medium coupled to the processor, configured tostore the plurality of intermediate images.
 13. The system of claim 12,wherein the processor is further configured to: acquire k-space datathrough magnetic resonance (MR) imaging; perform an inverse Fouriertransform on the acquired k-space data of to form an intermediate image;save the intermediate image in the standard format; and iterativelyperforming the steps of acquiring, performing, and saving until theplurality intermediate images are stored for the acquired k-space data.14. The system of claim 12, wherein the processor is further configuredto: perform a Computed Tomography (CT) medical imaging scan on an areaof interest of a patient; acquire a plurality of projections from the CTmedical imaging scan; filter each of the plurality of projections; andperform a Fourier transform on each of the projections to form theplurality of intermediate images.
 15. The system of claim 12, furthercomprising a display configured to display the reconstructed image. 16.The system of claim 12, wherein the selection of user-defined parametersincludes at least one of spatial resolution parameters and temporalresolution parameters.
 17. The system of claim 16, wherein the userinterface is configured to receive another selection of user-definedparameters having one of adjusted spatial resolution parameters oradjusted temporal resolution parameters, and the processor is configuredto retrieve and reconstruct the reconstructed image by aggregatinganother set of the plurality of the intermediate images, the another setcorresponding to the another selection.
 18. The system of claim 12,wherein the selection of user-defined parameters further includes atleast one of spatial distribution parameters and temporal distributionparameters.
 19. The system of claim 18, wherein the user interface isconfigured to receive a selection of parameters for a hyper-program, andwherein the processor is further configured to execute the hyper-programconfigured to analyze the plurality intermediate images to retrieve andreconstruct an image having one of a resolution on a scholastic scale ora temporal range, associated with the plurality of intermediate images.20. The system of claim 12, wherein the user interface is configured toreceive a search selection for selectively searching the intermediateimages for data having specific data content.
 21. The system of claim12, wherein the user interface is configured to receive a selectionidentifying a field of interest within the reconstructed image, and theprocessor is configured to retrieve and reconstruct the reconstructedimage by aggregating another set of the plurality of the intermediateimages, the another set corresponding to the selection identifying thefield of interest.